Overview

Dataset statistics

Number of variables20
Number of observations4250
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory664.2 KiB
Average record size in memory160.0 B

Variable types

Categorical2
Numeric15
Boolean3

Alerts

state has a high cardinality: 51 distinct valuesHigh cardinality
number_vmail_messages is highly overall correlated with voice_mail_planHigh correlation
total_day_minutes is highly overall correlated with total_day_chargeHigh correlation
total_day_charge is highly overall correlated with total_day_minutesHigh correlation
total_eve_minutes is highly overall correlated with total_eve_chargeHigh correlation
total_eve_charge is highly overall correlated with total_eve_minutesHigh correlation
total_night_minutes is highly overall correlated with total_night_chargeHigh correlation
total_night_charge is highly overall correlated with total_night_minutesHigh correlation
total_intl_minutes is highly overall correlated with total_intl_chargeHigh correlation
total_intl_charge is highly overall correlated with total_intl_minutesHigh correlation
voice_mail_plan is highly overall correlated with number_vmail_messagesHigh correlation
international_plan is highly imbalanced (55.3%)Imbalance
number_vmail_messages has 3139 (73.9%) zerosZeros
number_customer_service_calls has 886 (20.8%) zerosZeros

Reproduction

Analysis started2023-12-27 15:39:33.224335
Analysis finished2023-12-27 15:40:52.494919
Duration1 minute and 19.27 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

state
Categorical

Distinct51
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size33.3 KiB
WV
 
139
MN
 
108
ID
 
106
AL
 
101
VA
 
100
Other values (46)
3696 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters8500
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOH
2nd rowNJ
3rd rowOH
4th rowOK
5th rowMA

Common Values

ValueCountFrequency (%)
WV 139
 
3.3%
MN 108
 
2.5%
ID 106
 
2.5%
AL 101
 
2.4%
VA 100
 
2.4%
OR 99
 
2.3%
TX 98
 
2.3%
UT 97
 
2.3%
NY 96
 
2.3%
NJ 96
 
2.3%
Other values (41) 3210
75.5%

Length

2023-12-27T21:10:52.836546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wv 139
 
3.3%
mn 108
 
2.5%
id 106
 
2.5%
al 101
 
2.4%
va 100
 
2.4%
or 99
 
2.3%
tx 98
 
2.3%
ut 97
 
2.3%
ny 96
 
2.3%
nj 96
 
2.3%
Other values (41) 3210
75.5%

Most occurring characters

ValueCountFrequency (%)
N 921
 
10.8%
A 880
 
10.4%
M 779
 
9.2%
I 675
 
7.9%
T 528
 
6.2%
D 486
 
5.7%
O 432
 
5.1%
C 431
 
5.1%
V 408
 
4.8%
W 408
 
4.8%
Other values (14) 2552
30.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 8500
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 921
 
10.8%
A 880
 
10.4%
M 779
 
9.2%
I 675
 
7.9%
T 528
 
6.2%
D 486
 
5.7%
O 432
 
5.1%
C 431
 
5.1%
V 408
 
4.8%
W 408
 
4.8%
Other values (14) 2552
30.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8500
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 921
 
10.8%
A 880
 
10.4%
M 779
 
9.2%
I 675
 
7.9%
T 528
 
6.2%
D 486
 
5.7%
O 432
 
5.1%
C 431
 
5.1%
V 408
 
4.8%
W 408
 
4.8%
Other values (14) 2552
30.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 921
 
10.8%
A 880
 
10.4%
M 779
 
9.2%
I 675
 
7.9%
T 528
 
6.2%
D 486
 
5.7%
O 432
 
5.1%
C 431
 
5.1%
V 408
 
4.8%
W 408
 
4.8%
Other values (14) 2552
30.0%

account_length
Real number (ℝ)

Distinct215
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.23624
Minimum1
Maximum243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2023-12-27T21:10:53.163546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35.45
Q173
median100
Q3127
95-th percentile167
Maximum243
Range242
Interquartile range (IQR)54

Descriptive statistics

Standard deviation39.698401
Coefficient of variation (CV)0.3960484
Kurtosis-0.13217477
Mean100.23624
Median Absolute Deviation (MAD)27
Skewness0.12232732
Sum426004
Variance1575.963
MonotonicityNot monotonic
2023-12-27T21:10:53.532568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 53
 
1.2%
87 51
 
1.2%
93 50
 
1.2%
105 48
 
1.1%
100 48
 
1.1%
120 48
 
1.1%
116 47
 
1.1%
98 47
 
1.1%
127 47
 
1.1%
112 46
 
1.1%
Other values (205) 3765
88.6%
ValueCountFrequency (%)
1 7
0.2%
2 2
 
< 0.1%
3 7
0.2%
4 2
 
< 0.1%
5 2
 
< 0.1%
6 2
 
< 0.1%
7 5
0.1%
8 1
 
< 0.1%
9 2
 
< 0.1%
10 3
0.1%
ValueCountFrequency (%)
243 1
 
< 0.1%
232 2
< 0.1%
225 2
< 0.1%
224 2
< 0.1%
222 2
< 0.1%
221 1
 
< 0.1%
217 3
0.1%
216 1
 
< 0.1%
215 1
 
< 0.1%
212 1
 
< 0.1%

area_code
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size33.3 KiB
area_code_415
2108 
area_code_408
1086 
area_code_510
1056 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters55250
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowarea_code_415
2nd rowarea_code_415
3rd rowarea_code_408
4th rowarea_code_415
5th rowarea_code_510

Common Values

ValueCountFrequency (%)
area_code_415 2108
49.6%
area_code_408 1086
25.6%
area_code_510 1056
24.8%

Length

2023-12-27T21:10:53.868546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-27T21:10:54.308546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
area_code_415 2108
49.6%
area_code_408 1086
25.6%
area_code_510 1056
24.8%

Most occurring characters

ValueCountFrequency (%)
a 8500
15.4%
e 8500
15.4%
_ 8500
15.4%
r 4250
7.7%
c 4250
7.7%
o 4250
7.7%
d 4250
7.7%
4 3194
 
5.8%
1 3164
 
5.7%
5 3164
 
5.7%
Other values (2) 3228
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 34000
61.5%
Decimal Number 12750
 
23.1%
Connector Punctuation 8500
 
15.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 8500
25.0%
e 8500
25.0%
r 4250
12.5%
c 4250
12.5%
o 4250
12.5%
d 4250
12.5%
Decimal Number
ValueCountFrequency (%)
4 3194
25.1%
1 3164
24.8%
5 3164
24.8%
0 2142
16.8%
8 1086
 
8.5%
Connector Punctuation
ValueCountFrequency (%)
_ 8500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 34000
61.5%
Common 21250
38.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 8500
25.0%
e 8500
25.0%
r 4250
12.5%
c 4250
12.5%
o 4250
12.5%
d 4250
12.5%
Common
ValueCountFrequency (%)
_ 8500
40.0%
4 3194
 
15.0%
1 3164
 
14.9%
5 3164
 
14.9%
0 2142
 
10.1%
8 1086
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 55250
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 8500
15.4%
e 8500
15.4%
_ 8500
15.4%
r 4250
7.7%
c 4250
7.7%
o 4250
7.7%
d 4250
7.7%
4 3194
 
5.8%
1 3164
 
5.7%
5 3164
 
5.7%
Other values (2) 3228
 
5.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
False
3854 
True
396 
ValueCountFrequency (%)
False 3854
90.7%
True 396
 
9.3%
2023-12-27T21:10:54.588568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
False
3138 
True
1112 
ValueCountFrequency (%)
False 3138
73.8%
True 1112
 
26.2%
2023-12-27T21:10:54.842553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

number_vmail_messages
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6317647
Minimum0
Maximum52
Zeros3139
Zeros (%)73.9%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2023-12-27T21:10:55.105545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q316
95-th percentile36
Maximum52
Range52
Interquartile range (IQR)16

Descriptive statistics

Standard deviation13.439882
Coefficient of variation (CV)1.7610451
Kurtosis0.27303834
Mean7.6317647
Median Absolute Deviation (MAD)0
Skewness1.373091
Sum32435
Variance180.63043
MonotonicityNot monotonic
2023-12-27T21:10:55.420568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 3139
73.9%
31 69
 
1.6%
28 58
 
1.4%
24 57
 
1.3%
29 57
 
1.3%
33 55
 
1.3%
27 54
 
1.3%
26 53
 
1.2%
30 47
 
1.1%
32 47
 
1.1%
Other values (36) 614
 
14.4%
ValueCountFrequency (%)
0 3139
73.9%
4 1
 
< 0.1%
6 2
 
< 0.1%
8 2
 
< 0.1%
10 4
 
0.1%
11 2
 
< 0.1%
12 10
 
0.2%
13 3
 
0.1%
14 7
 
0.2%
15 12
 
0.3%
ValueCountFrequency (%)
52 1
 
< 0.1%
50 2
 
< 0.1%
49 3
 
0.1%
48 4
 
0.1%
47 4
 
0.1%
46 7
0.2%
45 10
0.2%
44 7
0.2%
43 13
0.3%
42 17
0.4%

total_day_minutes
Real number (ℝ)

Distinct1843
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.2596
Minimum0
Maximum351.5
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2023-12-27T21:10:55.730568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile91.59
Q1143.325
median180.45
Q3216.2
95-th percentile271.055
Maximum351.5
Range351.5
Interquartile range (IQR)72.875

Descriptive statistics

Standard deviation54.012373
Coefficient of variation (CV)0.2996366
Kurtosis-0.056709716
Mean180.2596
Median Absolute Deviation (MAD)36.6
Skewness-0.0069102298
Sum766103.3
Variance2917.3365
MonotonicityNot monotonic
2023-12-27T21:10:56.040568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
189.3 10
 
0.2%
180 9
 
0.2%
154 8
 
0.2%
177.1 8
 
0.2%
184.5 8
 
0.2%
209.9 7
 
0.2%
189.8 7
 
0.2%
138.7 7
 
0.2%
165.4 7
 
0.2%
174 7
 
0.2%
Other values (1833) 4172
98.2%
ValueCountFrequency (%)
0 2
< 0.1%
2.6 1
< 0.1%
6.6 1
< 0.1%
7.2 1
< 0.1%
7.8 1
< 0.1%
7.9 1
< 0.1%
25.9 1
< 0.1%
27 1
< 0.1%
29.9 1
< 0.1%
30.9 1
< 0.1%
ValueCountFrequency (%)
351.5 1
< 0.1%
346.8 1
< 0.1%
345.3 1
< 0.1%
338.4 1
< 0.1%
337.4 1
< 0.1%
335.5 1
< 0.1%
334.3 1
< 0.1%
332.9 1
< 0.1%
332.1 1
< 0.1%
329.8 1
< 0.1%

total_day_calls
Real number (ℝ)

Distinct120
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.907294
Minimum0
Maximum165
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2023-12-27T21:10:56.554565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q187
median100
Q3113
95-th percentile133
Maximum165
Range165
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.850817
Coefficient of variation (CV)0.19869237
Kurtosis0.19359365
Mean99.907294
Median Absolute Deviation (MAD)13
Skewness-0.085812463
Sum424606
Variance394.05495
MonotonicityNot monotonic
2023-12-27T21:10:56.860567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 101
 
2.4%
95 97
 
2.3%
110 92
 
2.2%
94 92
 
2.2%
112 90
 
2.1%
102 89
 
2.1%
97 88
 
2.1%
107 87
 
2.0%
100 85
 
2.0%
101 84
 
2.0%
Other values (110) 3345
78.7%
ValueCountFrequency (%)
0 2
< 0.1%
30 1
 
< 0.1%
34 1
 
< 0.1%
35 1
 
< 0.1%
36 1
 
< 0.1%
40 2
< 0.1%
42 1
 
< 0.1%
44 4
0.1%
45 3
0.1%
46 1
 
< 0.1%
ValueCountFrequency (%)
165 1
 
< 0.1%
160 2
 
< 0.1%
158 2
 
< 0.1%
157 2
 
< 0.1%
156 3
 
0.1%
152 2
 
< 0.1%
151 6
0.1%
150 3
 
0.1%
148 6
0.1%
147 8
0.2%

total_day_charge
Real number (ℝ)

Distinct1843
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.644682
Minimum0
Maximum59.76
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2023-12-27T21:10:57.195568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.5735
Q124.365
median30.68
Q336.75
95-th percentile46.081
Maximum59.76
Range59.76
Interquartile range (IQR)12.385

Descriptive statistics

Standard deviation9.182096
Coefficient of variation (CV)0.29963097
Kurtosis-0.056584435
Mean30.644682
Median Absolute Deviation (MAD)6.225
Skewness-0.0069125262
Sum130239.9
Variance84.310888
MonotonicityNot monotonic
2023-12-27T21:10:57.497552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.18 10
 
0.2%
30.6 9
 
0.2%
26.18 8
 
0.2%
30.11 8
 
0.2%
31.37 8
 
0.2%
35.68 7
 
0.2%
32.27 7
 
0.2%
23.58 7
 
0.2%
28.12 7
 
0.2%
29.58 7
 
0.2%
Other values (1833) 4172
98.2%
ValueCountFrequency (%)
0 2
< 0.1%
0.44 1
< 0.1%
1.12 1
< 0.1%
1.22 1
< 0.1%
1.33 1
< 0.1%
1.34 1
< 0.1%
4.4 1
< 0.1%
4.59 1
< 0.1%
5.08 1
< 0.1%
5.25 1
< 0.1%
ValueCountFrequency (%)
59.76 1
< 0.1%
58.96 1
< 0.1%
58.7 1
< 0.1%
57.53 1
< 0.1%
57.36 1
< 0.1%
57.04 1
< 0.1%
56.83 1
< 0.1%
56.59 1
< 0.1%
56.46 1
< 0.1%
56.07 1
< 0.1%

total_eve_minutes
Real number (ℝ)

Distinct1773
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.17391
Minimum0
Maximum359.3
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2023-12-27T21:10:57.817564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile118.2
Q1165.925
median200.7
Q3233.775
95-th percentile282.71
Maximum359.3
Range359.3
Interquartile range (IQR)67.85

Descriptive statistics

Standard deviation50.249518
Coefficient of variation (CV)0.25102931
Kurtosis0.043453202
Mean200.17391
Median Absolute Deviation (MAD)33.7
Skewness-0.030414586
Sum850739.1
Variance2525.0141
MonotonicityNot monotonic
2023-12-27T21:10:58.130569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
230.9 10
 
0.2%
199.7 9
 
0.2%
194 9
 
0.2%
187.5 9
 
0.2%
169.9 9
 
0.2%
195.5 8
 
0.2%
221.1 8
 
0.2%
223.5 8
 
0.2%
211.5 8
 
0.2%
230 8
 
0.2%
Other values (1763) 4164
98.0%
ValueCountFrequency (%)
0 1
< 0.1%
22.3 1
< 0.1%
37.8 1
< 0.1%
41.7 1
< 0.1%
42.2 1
< 0.1%
42.5 1
< 0.1%
43.9 1
< 0.1%
47.3 1
< 0.1%
48.1 1
< 0.1%
49.2 1
< 0.1%
ValueCountFrequency (%)
359.3 1
< 0.1%
352.1 1
< 0.1%
351.6 1
< 0.1%
349.4 1
< 0.1%
348.5 1
< 0.1%
347.3 1
< 0.1%
345.1 1
< 0.1%
344.9 1
< 0.1%
344 1
< 0.1%
341.3 1
< 0.1%

total_eve_calls
Real number (ℝ)

Distinct123
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.17647
Minimum0
Maximum170
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2023-12-27T21:10:58.489544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q187
median100
Q3114
95-th percentile133
Maximum170
Range170
Interquartile range (IQR)27

Descriptive statistics

Standard deviation19.908591
Coefficient of variation (CV)0.1987352
Kurtosis0.11459972
Mean100.17647
Median Absolute Deviation (MAD)13
Skewness-0.020811824
Sum425750
Variance396.352
MonotonicityNot monotonic
2023-12-27T21:10:58.850548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 98
 
2.3%
103 96
 
2.3%
91 95
 
2.2%
97 91
 
2.1%
108 88
 
2.1%
94 88
 
2.1%
96 88
 
2.1%
88 87
 
2.0%
101 86
 
2.0%
104 85
 
2.0%
Other values (113) 3348
78.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
12 1
 
< 0.1%
36 1
 
< 0.1%
38 1
 
< 0.1%
43 1
 
< 0.1%
44 2
 
< 0.1%
45 1
 
< 0.1%
46 5
0.1%
47 1
 
< 0.1%
48 6
0.1%
ValueCountFrequency (%)
170 1
 
< 0.1%
169 1
 
< 0.1%
168 1
 
< 0.1%
159 1
 
< 0.1%
157 1
 
< 0.1%
156 1
 
< 0.1%
155 5
0.1%
154 3
0.1%
153 1
 
< 0.1%
152 6
0.1%

total_eve_charge
Real number (ℝ)

Distinct1572
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.015012
Minimum0
Maximum30.54
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2023-12-27T21:10:59.514551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.05
Q114.1025
median17.06
Q319.8675
95-th percentile24.031
Maximum30.54
Range30.54
Interquartile range (IQR)5.765

Descriptive statistics

Standard deviation4.271212
Coefficient of variation (CV)0.2510261
Kurtosis0.043329494
Mean17.015012
Median Absolute Deviation (MAD)2.86
Skewness-0.030387891
Sum72313.8
Variance18.243252
MonotonicityNot monotonic
2023-12-27T21:10:59.892554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.25 13
 
0.3%
18.79 13
 
0.3%
16.12 13
 
0.3%
15.9 12
 
0.3%
16.97 12
 
0.3%
18.96 11
 
0.3%
17.09 10
 
0.2%
16.8 10
 
0.2%
19.63 10
 
0.2%
17.54 9
 
0.2%
Other values (1562) 4137
97.3%
ValueCountFrequency (%)
0 1
< 0.1%
1.9 1
< 0.1%
3.21 1
< 0.1%
3.54 1
< 0.1%
3.59 1
< 0.1%
3.61 1
< 0.1%
3.73 1
< 0.1%
4.02 1
< 0.1%
4.09 1
< 0.1%
4.18 1
< 0.1%
ValueCountFrequency (%)
30.54 1
< 0.1%
29.93 1
< 0.1%
29.89 1
< 0.1%
29.7 1
< 0.1%
29.62 1
< 0.1%
29.52 1
< 0.1%
29.33 1
< 0.1%
29.32 1
< 0.1%
29.24 1
< 0.1%
29.01 1
< 0.1%

total_night_minutes
Real number (ℝ)

Distinct1757
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.52788
Minimum0
Maximum395
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2023-12-27T21:11:00.276545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile118.09
Q1167.225
median200.45
Q3234.7
95-th percentile282.71
Maximum395
Range395
Interquartile range (IQR)67.475

Descriptive statistics

Standard deviation50.353548
Coefficient of variation (CV)0.25110497
Kurtosis0.11485358
Mean200.52788
Median Absolute Deviation (MAD)33.55
Skewness0.0084908193
Sum852243.5
Variance2535.4798
MonotonicityNot monotonic
2023-12-27T21:11:00.644548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
186.2 11
 
0.3%
208.9 10
 
0.2%
240 8
 
0.2%
224.7 8
 
0.2%
190.5 8
 
0.2%
228.1 8
 
0.2%
194.3 8
 
0.2%
193.6 8
 
0.2%
221.7 8
 
0.2%
169.4 8
 
0.2%
Other values (1747) 4165
98.0%
ValueCountFrequency (%)
0 1
< 0.1%
23.2 1
< 0.1%
43.7 1
< 0.1%
45 1
< 0.1%
46.7 1
< 0.1%
47.4 1
< 0.1%
50.1 2
< 0.1%
53.3 1
< 0.1%
54 1
< 0.1%
54.5 1
< 0.1%
ValueCountFrequency (%)
395 1
< 0.1%
381.9 1
< 0.1%
381.6 1
< 0.1%
377.5 1
< 0.1%
367.7 1
< 0.1%
364.9 1
< 0.1%
359.9 1
< 0.1%
355.1 1
< 0.1%
352.5 1
< 0.1%
352.2 1
< 0.1%

total_night_calls
Real number (ℝ)

Distinct128
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.839529
Minimum0
Maximum175
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2023-12-27T21:11:00.998904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q186
median100
Q3113
95-th percentile132
Maximum175
Range175
Interquartile range (IQR)27

Descriptive statistics

Standard deviation20.09322
Coefficient of variation (CV)0.20125515
Kurtosis0.077218359
Mean99.839529
Median Absolute Deviation (MAD)14
Skewness0.0052731102
Sum424318
Variance403.73748
MonotonicityNot monotonic
2023-12-27T21:11:01.318904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 100
 
2.4%
99 92
 
2.2%
95 91
 
2.1%
102 90
 
2.1%
94 88
 
2.1%
91 88
 
2.1%
98 87
 
2.0%
104 87
 
2.0%
100 86
 
2.0%
109 85
 
2.0%
Other values (118) 3356
79.0%
ValueCountFrequency (%)
0 1
 
< 0.1%
33 1
 
< 0.1%
36 1
 
< 0.1%
38 2
< 0.1%
40 1
 
< 0.1%
41 1
 
< 0.1%
42 4
0.1%
43 1
 
< 0.1%
44 1
 
< 0.1%
46 3
0.1%
ValueCountFrequency (%)
175 1
< 0.1%
170 1
< 0.1%
165 1
< 0.1%
164 1
< 0.1%
161 1
< 0.1%
160 1
< 0.1%
159 2
< 0.1%
158 2
< 0.1%
157 2
< 0.1%
156 2
< 0.1%

total_night_charge
Real number (ℝ)

Distinct992
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0238918
Minimum0
Maximum17.77
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2023-12-27T21:11:01.643897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.3145
Q17.5225
median9.02
Q310.56
95-th percentile12.7255
Maximum17.77
Range17.77
Interquartile range (IQR)3.0375

Descriptive statistics

Standard deviation2.2659218
Coefficient of variation (CV)0.2511025
Kurtosis0.11486517
Mean9.0238918
Median Absolute Deviation (MAD)1.51
Skewness0.008444754
Sum38351.54
Variance5.1344017
MonotonicityNot monotonic
2023-12-27T21:11:01.957920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.4 18
 
0.4%
10.8 17
 
0.4%
9.63 17
 
0.4%
8.15 17
 
0.4%
9.66 16
 
0.4%
9.76 15
 
0.4%
8.82 15
 
0.4%
10.49 15
 
0.4%
7.69 14
 
0.3%
8.57 14
 
0.3%
Other values (982) 4092
96.3%
ValueCountFrequency (%)
0 1
< 0.1%
1.04 1
< 0.1%
1.97 1
< 0.1%
2.03 1
< 0.1%
2.1 1
< 0.1%
2.13 1
< 0.1%
2.25 2
< 0.1%
2.4 1
< 0.1%
2.43 1
< 0.1%
2.45 1
< 0.1%
ValueCountFrequency (%)
17.77 1
< 0.1%
17.19 1
< 0.1%
17.17 1
< 0.1%
16.99 1
< 0.1%
16.55 1
< 0.1%
16.42 1
< 0.1%
16.2 1
< 0.1%
15.98 1
< 0.1%
15.86 1
< 0.1%
15.85 1
< 0.1%

total_intl_minutes
Real number (ℝ)

Distinct168
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.256071
Minimum0
Maximum20
Zeros22
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2023-12-27T21:11:02.329921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.7
Q18.5
median10.3
Q312
95-th percentile14.6
Maximum20
Range20
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.7601017
Coefficient of variation (CV)0.26911883
Kurtosis0.70295119
Mean10.256071
Median Absolute Deviation (MAD)1.8
Skewness-0.24135954
Sum43588.3
Variance7.6181615
MonotonicityNot monotonic
2023-12-27T21:11:03.019900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.1 75
 
1.8%
9.8 73
 
1.7%
11.4 73
 
1.7%
10.2 72
 
1.7%
10.9 71
 
1.7%
11.3 70
 
1.6%
10.1 69
 
1.6%
9.7 68
 
1.6%
10.5 66
 
1.6%
9.5 66
 
1.6%
Other values (158) 3547
83.5%
ValueCountFrequency (%)
0 22
0.5%
0.4 1
 
< 0.1%
1.1 2
 
< 0.1%
1.3 1
 
< 0.1%
2 2
 
< 0.1%
2.1 2
 
< 0.1%
2.2 2
 
< 0.1%
2.4 1
 
< 0.1%
2.5 1
 
< 0.1%
2.6 1
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
19.7 2
< 0.1%
19.3 1
 
< 0.1%
19.2 1
 
< 0.1%
18.9 1
 
< 0.1%
18.5 1
 
< 0.1%
18.4 1
 
< 0.1%
18.3 1
 
< 0.1%
18.2 2
< 0.1%
18 3
0.1%

total_intl_calls
Real number (ℝ)

Distinct21
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4263529
Minimum0
Maximum20
Zeros22
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2023-12-27T21:11:03.799899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q36
95-th percentile9
Maximum20
Range20
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4630691
Coefficient of variation (CV)0.55645565
Kurtosis3.2632275
Mean4.4263529
Median Absolute Deviation (MAD)1
Skewness1.3601222
Sum18812
Variance6.0667095
MonotonicityNot monotonic
2023-12-27T21:11:04.436897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3 847
19.9%
4 795
18.7%
2 644
15.2%
5 598
14.1%
6 408
9.6%
7 272
 
6.4%
1 226
 
5.3%
8 153
 
3.6%
9 126
 
3.0%
10 59
 
1.4%
Other values (11) 122
 
2.9%
ValueCountFrequency (%)
0 22
 
0.5%
1 226
 
5.3%
2 644
15.2%
3 847
19.9%
4 795
18.7%
5 598
14.1%
6 408
9.6%
7 272
 
6.4%
8 153
 
3.6%
9 126
 
3.0%
ValueCountFrequency (%)
20 1
 
< 0.1%
19 1
 
< 0.1%
18 4
 
0.1%
17 1
 
< 0.1%
16 7
 
0.2%
15 9
 
0.2%
14 5
 
0.1%
13 16
0.4%
12 18
0.4%
11 38
0.9%

total_intl_charge
Real number (ℝ)

Distinct168
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7696541
Minimum0
Maximum5.4
Zeros22
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2023-12-27T21:11:04.903901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.54
Q12.3
median2.78
Q33.24
95-th percentile3.94
Maximum5.4
Range5.4
Interquartile range (IQR)0.94

Descriptive statistics

Standard deviation0.74520414
Coefficient of variation (CV)0.26906036
Kurtosis0.70332127
Mean2.7696541
Median Absolute Deviation (MAD)0.48
Skewness-0.24167067
Sum11771.03
Variance0.5553292
MonotonicityNot monotonic
2023-12-27T21:11:05.344899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 75
 
1.8%
2.65 73
 
1.7%
3.08 73
 
1.7%
2.75 72
 
1.7%
2.94 71
 
1.7%
3.05 70
 
1.6%
2.73 69
 
1.6%
2.62 68
 
1.6%
2.84 66
 
1.6%
2.57 66
 
1.6%
Other values (158) 3547
83.5%
ValueCountFrequency (%)
0 22
0.5%
0.11 1
 
< 0.1%
0.3 2
 
< 0.1%
0.35 1
 
< 0.1%
0.54 2
 
< 0.1%
0.57 2
 
< 0.1%
0.59 2
 
< 0.1%
0.65 1
 
< 0.1%
0.68 1
 
< 0.1%
0.7 1
 
< 0.1%
ValueCountFrequency (%)
5.4 1
 
< 0.1%
5.32 2
< 0.1%
5.21 1
 
< 0.1%
5.18 1
 
< 0.1%
5.1 1
 
< 0.1%
5 1
 
< 0.1%
4.97 1
 
< 0.1%
4.94 1
 
< 0.1%
4.91 2
< 0.1%
4.86 3
0.1%
Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5590588
Minimum0
Maximum9
Zeros886
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2023-12-27T21:11:05.652904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3114335
Coefficient of variation (CV)0.84117001
Kurtosis1.6556188
Mean1.5590588
Median Absolute Deviation (MAD)1
Skewness1.0826916
Sum6626
Variance1.7198579
MonotonicityNot monotonic
2023-12-27T21:11:05.893898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 1524
35.9%
2 947
22.3%
0 886
20.8%
3 558
 
13.1%
4 209
 
4.9%
5 81
 
1.9%
6 28
 
0.7%
7 13
 
0.3%
9 2
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
0 886
20.8%
1 1524
35.9%
2 947
22.3%
3 558
 
13.1%
4 209
 
4.9%
5 81
 
1.9%
6 28
 
0.7%
7 13
 
0.3%
8 2
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
9 2
 
< 0.1%
8 2
 
< 0.1%
7 13
 
0.3%
6 28
 
0.7%
5 81
 
1.9%
4 209
 
4.9%
3 558
 
13.1%
2 947
22.3%
1 1524
35.9%
0 886
20.8%

churn
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
False
3652 
True
598 
ValueCountFrequency (%)
False 3652
85.9%
True 598
 
14.1%
2023-12-27T21:11:06.267906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Interactions

2023-12-27T21:10:46.725754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:43.858850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:48.008594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:52.203587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:56.484587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:01.647588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:06.757590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:11.612302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:15.869304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:20.562034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:25.125690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:29.224704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:33.916682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:38.092754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:42.292758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:46.969754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:44.200666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:48.272588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:52.478585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:56.745586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:01.916585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:07.062588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:11.864302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:16.126327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:20.820023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:25.376685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:29.481685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:34.187685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:38.362757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:42.569755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:47.212758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:44.465780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:48.522591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:52.736590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:57.009587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:02.181587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:07.457591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:12.124316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:16.374302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:21.085064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:25.632688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:29.741684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:34.447684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:38.625764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:42.815753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:47.470752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:44.766591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:48.797589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:53.030587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:57.338585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:02.454585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:07.844592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:12.408303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:16.664301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:21.387087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:25.902684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:30.140686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:34.753685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:38.906756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:43.079756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:47.717760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:45.037585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:49.215587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:53.329586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:57.797585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:02.726588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:08.223593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:12.668303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:17.143304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:21.651122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:26.172686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:30.470701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:35.032704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:39.182756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:43.443762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:48.092763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:45.297591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:49.474590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:53.612589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:58.115585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:02.989595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:08.529588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:12.967302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:17.525324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:21.947125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:26.431683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:30.781706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:35.309706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:39.452774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:43.692756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:48.356758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:45.578593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:49.753607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:53.913601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:58.503593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:03.275590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:08.942300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:13.256302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:18.006303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:22.270376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:26.703683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:31.102701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:35.586685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:39.726763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:43.966755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:48.651757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:45.844594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:50.019587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:54.196587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:58.965584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:03.740587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:09.284307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:13.553307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:18.279301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:22.582244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:26.972706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:31.404684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:35.866692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:40.009762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:44.232755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:48.951755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:46.107586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:50.288588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:54.484594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:59.461590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:04.115587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:09.646297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:13.849300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:18.533301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:22.979292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:27.237684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:31.914707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:36.147689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:40.312757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:44.507780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:49.276755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:46.384585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:50.564587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:54.771587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:59.785587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:04.584589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:09.952304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:14.147305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:18.806303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:23.322315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:27.586685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:32.200692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:36.439683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:40.601754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:45.008758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:49.632752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:46.654589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:50.842604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:55.063585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:00.170589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:05.025588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:10.238303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:14.407313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:19.074325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:23.594368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:27.854705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:32.509689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:36.718682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:40.882776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:45.312754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:49.905757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:46.937592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:51.120586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:55.357586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:00.453596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:05.490594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:10.522304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:14.689308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:19.351299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:23.995369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:28.141684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:32.790701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:36.996696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:41.164764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:45.609755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:50.214756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:47.220592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:51.408587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:55.650588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:00.740585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:05.813588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:10.799305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:15.080302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:19.679783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:24.309397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:28.432706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:33.102688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:37.286685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:41.449762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:45.917755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:50.533758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:47.503592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:51.691587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:55.947585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:01.067588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:06.154587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:11.091305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:15.374303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:20.018870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:24.601689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:28.716708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:33.387687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:37.568685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:41.731756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:46.238753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:50.788777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:47.760585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:51.953588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:09:56.236591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:01.381587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:06.459592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:11.350323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:15.624303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:20.294036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:24.863692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:28.969685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:33.646704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:37.834694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:41.998758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-27T21:10:46.497756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-12-27T21:11:06.536897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
account_lengthnumber_vmail_messagestotal_day_minutestotal_day_callstotal_day_chargetotal_eve_minutestotal_eve_callstotal_eve_chargetotal_night_minutestotal_night_callstotal_night_chargetotal_intl_minutestotal_intl_callstotal_intl_chargenumber_customer_service_callsstatearea_codeinternational_planvoice_mail_planchurn
account_length1.000-0.0030.0000.0190.000-0.0140.005-0.014-0.007-0.001-0.0070.0120.0190.012-0.0070.0100.0080.0000.0000.000
number_vmail_messages-0.0031.0000.007-0.0110.0070.0150.0040.0150.0110.0060.0110.002-0.0050.002-0.0240.0000.0000.0000.9980.112
total_day_minutes0.0000.0071.0000.0031.000-0.0150.005-0.0150.002-0.0040.002-0.024-0.000-0.024-0.0070.0000.0380.0510.0360.372
total_day_calls0.019-0.0110.0031.0000.0030.0090.0100.0090.0020.0030.0020.0060.0080.006-0.0210.0000.0370.0000.0000.033
total_day_charge0.0000.0071.0000.0031.000-0.0150.005-0.0150.002-0.0040.002-0.024-0.000-0.024-0.0070.0000.0370.0520.0360.371
total_eve_minutes-0.0140.015-0.0150.009-0.0151.0000.0011.000-0.0190.013-0.0190.0060.0150.006-0.0140.0060.0000.0000.0190.077
total_eve_calls0.0050.0040.0050.0100.0050.0011.0000.0010.012-0.0160.012-0.022-0.001-0.0220.0100.0290.0000.0000.0000.000
total_eve_charge-0.0140.015-0.0150.009-0.0151.0000.0011.000-0.0190.013-0.0190.0060.0150.006-0.0140.0000.0000.0000.0170.077
total_night_minutes-0.0070.0110.0020.0020.002-0.0190.012-0.0191.0000.0111.000-0.002-0.020-0.002-0.0230.0170.0000.0400.0310.029
total_night_calls-0.0010.006-0.0040.003-0.0040.013-0.0160.0130.0111.0000.0110.0060.0040.006-0.0010.0070.0000.0000.0000.000
total_night_charge-0.0070.0110.0020.0020.002-0.0190.012-0.0191.0000.0111.000-0.002-0.020-0.002-0.0230.0090.0000.0380.0310.028
total_intl_minutes0.0120.002-0.0240.006-0.0240.006-0.0220.006-0.0020.006-0.0021.0000.0071.000-0.0180.0290.0190.0000.0000.051
total_intl_calls0.019-0.005-0.0000.008-0.0000.015-0.0010.015-0.0200.004-0.0200.0071.0000.007-0.0070.0080.0110.0000.0000.074
total_intl_charge0.0120.002-0.0240.006-0.0240.006-0.0220.006-0.0020.006-0.0021.0000.0071.000-0.0180.0290.0190.0000.0000.051
number_customer_service_calls-0.007-0.024-0.007-0.021-0.007-0.0140.010-0.014-0.023-0.001-0.023-0.018-0.007-0.0181.0000.0230.0000.0000.0380.315
state0.0100.0000.0000.0000.0000.0060.0290.0000.0170.0070.0090.0290.0080.0290.0231.0000.0160.0190.0000.092
area_code0.0080.0000.0380.0370.0370.0000.0000.0000.0000.0000.0000.0190.0110.0190.0000.0161.0000.0190.0000.000
international_plan0.0000.0000.0510.0000.0520.0000.0000.0000.0400.0000.0380.0000.0000.0000.0000.0190.0191.0000.0000.257
voice_mail_plan0.0000.9980.0360.0000.0360.0190.0000.0170.0310.0000.0310.0000.0000.0000.0380.0000.0000.0001.0000.113
churn0.0000.1120.3720.0330.3710.0770.0000.0770.0290.0000.0280.0510.0740.0510.3150.0920.0000.2570.1131.000

Missing values

2023-12-27T21:10:51.295386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-27T21:10:52.110383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

stateaccount_lengtharea_codeinternational_planvoice_mail_plannumber_vmail_messagestotal_day_minutestotal_day_callstotal_day_chargetotal_eve_minutestotal_eve_callstotal_eve_chargetotal_night_minutestotal_night_callstotal_night_chargetotal_intl_minutestotal_intl_callstotal_intl_chargenumber_customer_service_callschurn
0OH107area_code_415noyes26161.612327.47195.510316.62254.410311.4513.733.701no
1NJ137area_code_415nono0243.411441.38121.211010.30162.61047.3212.253.290no
2OH84area_code_408yesno0299.47150.9061.9885.26196.9898.866.671.782no
3OK75area_code_415yesno0166.711328.34148.312212.61186.91218.4110.132.733no
4MA121area_code_510noyes24218.28837.09348.510829.62212.61189.577.572.033no
5MO147area_code_415yesno0157.07926.69103.1948.76211.8969.537.161.920no
6LA117area_code_408nono0184.59731.37351.68029.89215.8909.718.742.351no
7WV141area_code_415yesyes37258.68443.96222.011118.87326.49714.6911.253.020no
8IN65area_code_415nono0129.113721.95228.58319.42208.81119.4012.763.434yes
9RI74area_code_415nono0187.712731.91163.414813.89196.0948.829.152.460no
stateaccount_lengtharea_codeinternational_planvoice_mail_plannumber_vmail_messagestotal_day_minutestotal_day_callstotal_day_chargetotal_eve_minutestotal_eve_callstotal_eve_chargetotal_night_minutestotal_night_callstotal_night_chargetotal_intl_minutestotal_intl_callstotal_intl_chargenumber_customer_service_callschurn
4240AR127area_code_415noyes27157.610726.79280.64923.8575.1773.388.042.161no
4241WA80area_code_510nono0157.010126.69208.812717.75113.31095.1016.224.372no
4242MN150area_code_408nono0170.011528.90162.713813.83267.27712.028.322.240no
4243ND140area_code_510nono0244.711541.60258.610121.98231.311210.417.562.031yes
4244AZ97area_code_510nono0252.68942.94340.39128.93256.56711.548.852.381yes
4245MT83area_code_415nono0188.37032.01243.88820.72213.7799.6210.362.780no
4246WV73area_code_408nono0177.98930.24131.28211.15186.2898.3811.563.113no
4247NC75area_code_408nono0170.710129.02193.112616.41129.11045.816.971.861no
4248HI50area_code_408noyes40235.712740.07223.012618.96297.511613.399.952.672no
4249VT86area_code_415noyes34129.410222.00267.110422.70154.81006.979.3162.510no